Simulation Research for Giant Magnetostrictive Actuator Controller Using Model Reference Control Based on Neural Network

摘要：

Giant Magnetostrictive Material (GMM) has inherent hysteretic nonlinearity, and its hysteretic performance changes with input frequency. Hence, it is difficult for a normal controller to control its actuator precisely. Due to this, a hysteretic compensation control strategy was proposed. Adopting neural network model reference, combine the dynamic model of Giant Magnetostrictive Actuator (GMA) as reference model, with BP neural network. Introducing error feed-back learning scheme BP into controller and identifier, controller can identify GMA and identifier control it precisely. To accelerate the convergence of the trace error, train the neural network offline.

关键词： Giant Magnetostrictive Actuator BP model reference control hysteric nonlinearity

作者: Yang Lingxiao Zhong Ying

作者单位: Henan polytechnic University, Jiaozuo, Henan, 454000, China

会议类型: 国际会议

会议名称: 2010 International Conference on Intelligent Computation Technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

会议地点: 长沙

会议语种:英文

页码: 2703-2705

在线出版日期: 2010-05-11（万方平台首次上网日期，不代表论文的发表时间）